{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "38a72ebc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "batch_size = 8\n",
    "train_dataset = torchvision.datasets.cifar.CIFAR100(root='cifar100', train=False, transform=transform, download=True)\n",
    "test_dataset = torchvision.datasets.cifar.CIFAR100(root='cifar100', train=False, transform=transform, download=True)\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50090097",
   "metadata": {},
   "source": [
    "## 展示数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b43b2efc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32])\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# functions to show an image\n",
    "\n",
    "\n",
    "def imshow(img):\n",
    "    img = img / 2 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# get some random training images\n",
    "dataiter = iter(train_loader)\n",
    "# images, labels = dataiter.next()\n",
    "images, labels = next(dataiter)\n",
    "\n",
    "# show images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "# print labels\n",
    "print(labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2402ebdf",
   "metadata": {},
   "source": [
    "## 定义模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2efe2126",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet(\n",
      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (relu): ReLU(inplace=True)\n",
      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "  (layer1): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU()\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU()\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "    (2): BasicBlock(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU()\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "  )\n",
      "  (layer2): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU()\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU()\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "    (2): BasicBlock(\n",
      "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU()\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "    (3): BasicBlock(\n",
      "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU()\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "  )\n",
      "  (layer3): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU()\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU()\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "    (2): BasicBlock(\n",
      "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU()\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "    (3): BasicBlock(\n",
      "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU()\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "    (4): BasicBlock(\n",
      "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU()\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "    (5): BasicBlock(\n",
      "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU()\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "  )\n",
      "  (layer4): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU()\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU()\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "    (2): BasicBlock(\n",
      "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU()\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  (fc): Linear(in_features=512, out_features=100, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class BasicBlock(nn.Module):\n",
    "    expansion = 1\n",
    "\n",
    "    def __init__(self, in_channel, out_channel, stride=1, downsample=None, **kwargs):\n",
    "        super(BasicBlock, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,\n",
    "                               kernel_size=3, stride=stride, padding=1, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(out_channel)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel,\n",
    "                               kernel_size=3, stride=1, padding=1, bias=False)\n",
    "        self.bn2 = nn.BatchNorm2d(out_channel)\n",
    "        self.downsample = downsample\n",
    "\n",
    "    def forward(self, x):\n",
    "        identity = x\n",
    "        if self.downsample is not None:\n",
    "            identity = self.downsample(x)\n",
    "\n",
    "        out = self.conv1(x)\n",
    "        out = self.bn1(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv2(out)\n",
    "        out = self.bn2(out)\n",
    "\n",
    "        out += identity\n",
    "        out = self.relu(out)\n",
    "\n",
    "        return out\n",
    "\n",
    "\n",
    "class Bottleneck(nn.Module):\n",
    "\n",
    "    expansion = 4\n",
    "\n",
    "    def __init__(self, in_channel, out_channel, stride=1, downsample=None, groups=1, width_per_group=64):\n",
    "        super(Bottleneck, self).__init__()\n",
    "\n",
    "        width = int(out_channel * (width_per_group / 64.)) * groups\n",
    "\n",
    "        self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=width,\n",
    "                               kernel_size=1, stride=1, bias=False)  # squeeze channels\n",
    "        self.bn1 = nn.BatchNorm2d(width)\n",
    "        # -----------------------------------------\n",
    "        self.conv2 = nn.Conv2d(in_channels=width, out_channels=width, groups=groups,\n",
    "                               kernel_size=3, stride=stride, bias=False, padding=1)\n",
    "        self.bn2 = nn.BatchNorm2d(width)\n",
    "        # -----------------------------------------\n",
    "        self.conv3 = nn.Conv2d(in_channels=width, out_channels=out_channel*self.expansion,\n",
    "                               kernel_size=1, stride=1, bias=False)  # unsqueeze channels\n",
    "        self.bn3 = nn.BatchNorm2d(out_channel*self.expansion)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        self.downsample = downsample\n",
    "\n",
    "    def forward(self, x):\n",
    "        identity = x\n",
    "        if self.downsample is not None:\n",
    "            identity = self.downsample(x)\n",
    "\n",
    "        out = self.conv1(x)\n",
    "        out = self.bn1(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv2(out)\n",
    "        out = self.bn2(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv3(out)\n",
    "        out = self.bn3(out)\n",
    "\n",
    "        out += identity\n",
    "        out = self.relu(out)\n",
    "\n",
    "        return out\n",
    "\n",
    "\n",
    "class ResNet(nn.Module):\n",
    "\n",
    "    def __init__(self, block, blocks_num, num_classes=1000):\n",
    "        super(ResNet, self).__init__()\n",
    "        self.in_channel = 64\n",
    "\n",
    "        self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2, padding=3, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(self.in_channel)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
    "        self.layer1 = self._make_layer(block, 64, blocks_num[0])\n",
    "        self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2)\n",
    "        self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2)\n",
    "        self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2)\n",
    "        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n",
    "        self.fc = nn.Linear(512 * block.expansion, num_classes)\n",
    "\n",
    "    def _make_layer(self, block, channel, block_num, stride=1):\n",
    "        downsample = None\n",
    "        if stride != 1 or self.in_channel != channel * block.expansion:\n",
    "            downsample = nn.Sequential(\n",
    "                nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False),\n",
    "                nn.BatchNorm2d(channel * block.expansion))\n",
    "\n",
    "        layers = []\n",
    "        layers.append(block(self.in_channel,\n",
    "                            channel,\n",
    "                            downsample=downsample,\n",
    "                            stride=stride))\n",
    "        self.in_channel = channel * block.expansion\n",
    "\n",
    "        for _ in range(1, block_num):\n",
    "            layers.append(block(self.in_channel, channel))\n",
    "\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.bn1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.maxpool(x)\n",
    "\n",
    "        x = self.layer1(x)\n",
    "        x = self.layer2(x)\n",
    "        x = self.layer3(x)\n",
    "        x = self.layer4(x)\n",
    "\n",
    "        x = self.avgpool(x)\n",
    "        x = torch.flatten(x, 1)\n",
    "        x = self.fc(x)\n",
    "\n",
    "        return x\n",
    "\n",
    "\n",
    "def resnet34(num_classes=100):\n",
    "    return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes)\n",
    "\n",
    "def smallnet(num_classes=100):\n",
    "    return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes)\n",
    "\n",
    "net = resnet34()\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1b72b33",
   "metadata": {},
   "source": [
    "## 定义优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "af29e5e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313\n"
     ]
    }
   ],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)\n",
    "print(len(train_loader))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bc43de9",
   "metadata": {},
   "source": [
    "## 训练网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "df16afc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1,   100] top1Accuracy: 3.25 %\n",
      "[1,   100] top5Accuracy: 0.0 %\n",
      "[1,   100] loss: 0.247\n",
      "[1,   200] top1Accuracy: 4.43 %\n",
      "[1,   200] top5Accuracy: 0.0 %\n",
      "[1,   200] loss: 0.232\n",
      "[1,   300] top1Accuracy: 4.62 %\n",
      "[1,   300] top5Accuracy: 0.0 %\n",
      "[1,   300] loss: 0.224\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x000002EE9AD49F70>\n",
      "Traceback (most recent call last):\n",
      "  File \"d:\\AcademicLife\\miniconda\\envs\\py38\\lib\\site-packages\\torch\\utils\\data\\dataloader.py\", line 1466, in __del__\n",
      "    self._shutdown_workers()\n",
      "  File \"d:\\AcademicLife\\miniconda\\envs\\py38\\lib\\site-packages\\torch\\utils\\data\\dataloader.py\", line 1424, in _shutdown_workers\n",
      "    if self._persistent_workers or self._workers_status[worker_id]:\n",
      "AttributeError: '_MultiProcessingDataLoaderIter' object has no attribute '_workers_status'\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[17], line 34\u001b[0m\n\u001b[0;32m     32\u001b[0m top5Correct \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m     33\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[1;32m---> 34\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m data \u001b[38;5;129;01min\u001b[39;00m test_loader:\n\u001b[0;32m     35\u001b[0m         images, labels \u001b[38;5;241m=\u001b[39m data\n\u001b[0;32m     36\u001b[0m         \u001b[38;5;66;03m# calculate outputs by running images through the network\u001b[39;00m\n",
      "File \u001b[1;32md:\\AcademicLife\\miniconda\\envs\\py38\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:435\u001b[0m, in \u001b[0;36mDataLoader.__iter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    433\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_iterator\n\u001b[0;32m    434\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 435\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_iterator()\n",
      "File \u001b[1;32md:\\AcademicLife\\miniconda\\envs\\py38\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:381\u001b[0m, in \u001b[0;36mDataLoader._get_iterator\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    379\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    380\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcheck_worker_number_rationality()\n\u001b[1;32m--> 381\u001b[0m     \u001b[39mreturn\u001b[39;00m _MultiProcessingDataLoaderIter(\u001b[39mself\u001b[39;49m)\n",
      "File \u001b[1;32md:\\AcademicLife\\miniconda\\envs\\py38\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:1034\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter.__init__\u001b[1;34m(self, loader)\u001b[0m\n\u001b[0;32m   1027\u001b[0m w\u001b[39m.\u001b[39mdaemon \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m   1028\u001b[0m \u001b[39m# NB: Process.start() actually take some time as it needs to\u001b[39;00m\n\u001b[0;32m   1029\u001b[0m \u001b[39m#     start a process and pass the arguments over via a pipe.\u001b[39;00m\n\u001b[0;32m   1030\u001b[0m \u001b[39m#     Therefore, we only add a worker to self._workers list after\u001b[39;00m\n\u001b[0;32m   1031\u001b[0m \u001b[39m#     it started, so that we do not call .join() if program dies\u001b[39;00m\n\u001b[0;32m   1032\u001b[0m \u001b[39m#     before it starts, and __del__ tries to join but will get:\u001b[39;00m\n\u001b[0;32m   1033\u001b[0m \u001b[39m#     AssertionError: can only join a started process.\u001b[39;00m\n\u001b[1;32m-> 1034\u001b[0m w\u001b[39m.\u001b[39;49mstart()\n\u001b[0;32m   1035\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_index_queues\u001b[39m.\u001b[39mappend(index_queue)\n\u001b[0;32m   1036\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_workers\u001b[39m.\u001b[39mappend(w)\n",
      "File \u001b[1;32md:\\AcademicLife\\miniconda\\envs\\py38\\lib\\multiprocessing\\process.py:121\u001b[0m, in \u001b[0;36mBaseProcess.start\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    118\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mnot\u001b[39;00m _current_process\u001b[39m.\u001b[39m_config\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39mdaemon\u001b[39m\u001b[39m'\u001b[39m), \\\n\u001b[0;32m    119\u001b[0m        \u001b[39m'\u001b[39m\u001b[39mdaemonic processes are not allowed to have children\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m    120\u001b[0m _cleanup()\n\u001b[1;32m--> 121\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_popen \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_Popen(\u001b[39mself\u001b[39;49m)\n\u001b[0;32m    122\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sentinel \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_popen\u001b[39m.\u001b[39msentinel\n\u001b[0;32m    123\u001b[0m \u001b[39m# Avoid a refcycle if the target function holds an indirect\u001b[39;00m\n\u001b[0;32m    124\u001b[0m \u001b[39m# reference to the process object (see bpo-30775)\u001b[39;00m\n",
      "File \u001b[1;32md:\\AcademicLife\\miniconda\\envs\\py38\\lib\\multiprocessing\\context.py:224\u001b[0m, in \u001b[0;36mProcess._Popen\u001b[1;34m(process_obj)\u001b[0m\n\u001b[0;32m    222\u001b[0m \u001b[39m@staticmethod\u001b[39m\n\u001b[0;32m    223\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_Popen\u001b[39m(process_obj):\n\u001b[1;32m--> 224\u001b[0m     \u001b[39mreturn\u001b[39;00m _default_context\u001b[39m.\u001b[39;49mget_context()\u001b[39m.\u001b[39;49mProcess\u001b[39m.\u001b[39;49m_Popen(process_obj)\n",
      "File \u001b[1;32md:\\AcademicLife\\miniconda\\envs\\py38\\lib\\multiprocessing\\context.py:327\u001b[0m, in \u001b[0;36mSpawnProcess._Popen\u001b[1;34m(process_obj)\u001b[0m\n\u001b[0;32m    324\u001b[0m \u001b[39m@staticmethod\u001b[39m\n\u001b[0;32m    325\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_Popen\u001b[39m(process_obj):\n\u001b[0;32m    326\u001b[0m     \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mpopen_spawn_win32\u001b[39;00m \u001b[39mimport\u001b[39;00m Popen\n\u001b[1;32m--> 327\u001b[0m     \u001b[39mreturn\u001b[39;00m Popen(process_obj)\n",
      "File \u001b[1;32md:\\AcademicLife\\miniconda\\envs\\py38\\lib\\multiprocessing\\popen_spawn_win32.py:93\u001b[0m, in \u001b[0;36mPopen.__init__\u001b[1;34m(self, process_obj)\u001b[0m\n\u001b[0;32m     91\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m     92\u001b[0m     reduction\u001b[39m.\u001b[39mdump(prep_data, to_child)\n\u001b[1;32m---> 93\u001b[0m     reduction\u001b[39m.\u001b[39;49mdump(process_obj, to_child)\n\u001b[0;32m     94\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     95\u001b[0m     set_spawning_popen(\u001b[39mNone\u001b[39;00m)\n",
      "File \u001b[1;32md:\\AcademicLife\\miniconda\\envs\\py38\\lib\\multiprocessing\\reduction.py:60\u001b[0m, in \u001b[0;36mdump\u001b[1;34m(obj, file, protocol)\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdump\u001b[39m(obj, file, protocol\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m     59\u001b[0m     \u001b[39m'''Replacement for pickle.dump() using ForkingPickler.'''\u001b[39;00m\n\u001b[1;32m---> 60\u001b[0m     ForkingPickler(file, protocol)\u001b[39m.\u001b[39;49mdump(obj)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "lossList = []\n",
    "top1AccuracyList = []   # top1准确率列表\n",
    "top5AccuracyList = []   # top5准确率列表\n",
    "max_epoch = 2\n",
    "for epoch in range(max_epoch):  # loop over the dataset multiple times\n",
    "\n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(train_loader, 0):\n",
    "        # get the inputs; data is a list of [inputs, labels]\n",
    "        inputs, labels = data\n",
    "#         print(labels)\n",
    "#         print(len(inputs))\n",
    "\n",
    "        # zero the parameter gradients\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # forward + backward + optimize\n",
    "        outputs = net(inputs)\n",
    "#         print(outputs)\n",
    "#         print(labels)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        for data in test_loader:\n",
    "            images, labels = data\n",
    "            # calculate outputs by running images through the network\n",
    "            outputs = net(images)\n",
    "            # the class with the highest energy is what we choose as prediction\n",
    "            _, top1Predicted = torch.max(outputs.data, 1)\n",
    "            top5Predicted = torch.topk(outputs.data, k=5, dim=1, largest=True)[0]\n",
    "            total += labels.size(0)\n",
    "            top1Correct += (top1Predicted == labels).sum().item()\n",
    "            label_resize = labels.view(-1, 1).expand_as(top5Predicted)\n",
    "            top5Correct += torch.eq(top5Predicted, label_resize).view(-1).sum().float().item()\n",
    "\n",
    "        # print statistics\n",
    "        running_loss += loss.item()\n",
    "        # 每100个mini-batch输出一次准确率\n",
    "        if i % 100 == 99:    # print every 100 mini-batches\n",
    "            # since we're not training, we don't need to calculate the gradients for our outputs\n",
    "            total = 0\n",
    "            top1Correct = 0\n",
    "            top5Correct = 0\n",
    "            with torch.no_grad():\n",
    "                for data in test_loader:\n",
    "                    images, labels = data\n",
    "                    # calculate outputs by running images through the network\n",
    "                    outputs = net(images)\n",
    "                    # the class with the highest energy is what we choose as prediction\n",
    "                    _, top1Predicted = torch.max(outputs.data, 1)\n",
    "                    top5Predicted = torch.topk(outputs.data, k=5, dim=1, largest=True)[0]\n",
    "                    total += labels.size(0)\n",
    "                    top1Correct += (top1Predicted == labels).sum().item()\n",
    "                    label_resize = labels.view(-1, 1).expand_as(top5Predicted)\n",
    "                    top5Correct += torch.eq(top5Predicted, label_resize).view(-1).sum().float().item()\n",
    "            print(f'[{epoch + 1}, {i + 1:5d}] top1Accuracy: {100 * top1Correct / total} %')\n",
    "            print(f'[{epoch + 1}, {i + 1:5d}] top5Accuracy: {100 * top5Correct / total} %')\n",
    "            print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')\n",
    "            top1AccuracyList.append(top1Correct / total)\n",
    "            top5AccuracyList.append(top5Correct / total)\n",
    "            lossList.append(running_loss / 100)\n",
    "            running_loss = 0.0\n",
    "\n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d660104",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 绘制曲线\n",
    "plt.figure(figsize=(5,3))\n",
    "plt.plot(np.arange(1,len(lossList)), lossList)\n",
    "plt.title('validation loss')\n",
    "\n",
    "plt.figure(figsize=(5,3))\n",
    "plt.plot(np.arange(1,len(top1AccuracyList)), top1AccuracyList)\n",
    "plt.title('validation top1 accuracy')\n",
    "\n",
    "plt.figure(figsize=(5,3))\n",
    "plt.plot(np.arange(1,len(top5AccuracyList)), top5AccuracyList)\n",
    "plt.title('validation top5 accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c507f975",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存模型\n",
    "PATH = './cifar_ResNet.pth'\n",
    "torch.save(net.state_dict(), PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8529d621",
   "metadata": {},
   "source": [
    "## 在测试集上测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b18091e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "top1Correct = 0\n",
    "top5Correct = 0\n",
    "total = 0\n",
    "# since we're not training, we don't need to calculate the gradients for our outputs\n",
    "with torch.no_grad():\n",
    "    for data in test_loader:\n",
    "        images, labels = data\n",
    "        # calculate outputs by running images through the network\n",
    "        outputs = net(images)\n",
    "        # the class with the highest energy is what we choose as prediction\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        top5Predicted = torch.topk(outputs.data, k=5, dim=1, largest=True)[0]\n",
    "        total += labels.size(0)\n",
    "        top1Correct += (predicted == labels).sum().item()\n",
    "        label_resize = labels.view(-1, 1).expand_as(top5Predicted)\n",
    "        top5Correct += torch.eq(top5Predicted, label_resize).view(-1).sum().float().item()\n",
    "\n",
    "print(f'Top1 Accuracy of the network on the  test images: {100 * top1Correct // total} %')\n",
    "print(f'Top5 Accuracy of the network on the  test images: {100 * top5Correct // total} %')"
   ]
  }
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